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7,184 results for “recurrence”
Data associated with "A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations"
<p><strong>Datasets used in <em>A weakened recurrent circuit in the hippocampus of Rett syndrome mice disrupts long-term memory representations.</em></strong></p> <p><strong>Datatypes:</strong></p> <ol> <li>Multi-index pandas dataframe (.pkl)</li> <li>Numpy array (.npy)</li> <li>Collection of numpy arrays (.npz)</li> <li>Python dictionary objects (.pkl)</li> </ol> <p><strong>Datasets:</strong></p> <p><strong>alignments.pkl: A dataframe containing numpy arrays of image displacements for each mouse in each memory context.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are ['T', 'F1', 'N1', 'F2', 'N2'] for the training, recall 1-hour, neutral, recall 1-day, neutral day 2 memory contexts respectively. Each element of this dataframe is a numpy array of shape images x 2 that hold x and y image displacements respectively. These alignments are computed after the inscopix software motion correction and are used in Supplemental Figure 2 of the paper.</p> <p><strong>behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id.The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context (*) in ('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2').</p> <p><strong>correlated_pairs_df.pkl: A dataframe containing arrays of neuron indices that have a correlation in activity pattern > 0.3.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a numpy array with three columns. The first two columns are the neuron indices that are correlated and the last column is the strength of the correlation.</p> <p><strong>dredd_freezes_df.pkl: A dataframe containing freezing percentages for SOM-Cre and RTT-SOM-Cre mice treated with DREADDS.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment (mcherry, hm3d, hm4d). The columns contain one of ['Neutral', 'Fear', 'Fear_2']. Each element of the dataframe is a freezing percentage for a single mouse. This dataframe is built from reading the dredd_behavior.xlsx excel file. This is used to generate figure 5E of the paper.</p> <p><strong>high_degree_df.pkl: A dataframe containing list of high degree neuron indices.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id and treatment ('NA'=not applicable since no DREADD used). The columns contain ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing each memory context. Each element of the dataframe is a list of neuron indices that are high-degree cells.</p> <p><strong>N006_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N006 of genotype wild-type.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Supplemental Figure 2 of the paper.</p> <p><strong>N006_wt_cxtbasis.pkl: A dictionary containing arrays for basis images and singular values for each context.</strong></p> <p>This dictionary has keys, ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] representing the memory contexts. Each value is a 2 element list containing the U-basis images as column vectors and singular values, one per basis image in U. The shape of the basis images is the same shape stored in N006_wt_basis.pkl. This dataset is used in Supplementary Figure 2 to track cells across contexts of the CFC task (see also N006_wt_cxtsources.pkl)</p> <p><strong>N006_wt_cxtsources.pkl: A dictionary containing the independent component source images computed from the basis images for automatically identifying regions of interest (ROIs). </strong></p> <p>The dictionary is keyed on ['Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'] contexts. Each value in the dictionary at a given key is a 3-D numpy array of shape sources x height x width. These data were used to construct the source images and max intensity projection image of the sources in Supplemental Figure 2F-J of the paper.</p> <p><strong>N006_wt_rois.pkl: A dictionary containing the boundaries and annuli coordinates of all rois for mouse N006 of genotype wild-type.</strong></p> <p>This dictionary is keyed on ['boundaries', 'annuli'] contexts and each value is a 179 element list of arrays of boundary line coordinates or annulus point coordinates one per ROI detected for this mouse.</p> <p><strong>N006_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N006 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=205 source images, height=517 pixels and width=704 pixels. This data was used to construct Supplemental Figure 3F.</p> <p><strong>N019_wt_basis.npz: a dict containing three numpy arrays representing the basis images for mouse N019 of genotype wild-type.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 220 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 1C of the paper.</p> <p><strong>N019_wt_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=204 source images, height=516 pixels and width=698 pixels. This data was used to construct Figure 1C of the paper.</p> <p><strong>P80_animals.pkl: A pandas multi-index object containing the genotype, mouse_id and treatment of the top 80% behavioral performance animals.</strong></p> <p>In this study, we drop the lowest 20% performing WT and RTT animals based on freezing percentage during the recall contexts. This multi-index is used to filter the data before each computation or plot in this study. So for example Figure 1B contains only the top 80% performing WT and RTT mice.</p> <p><strong>pc_sipscs_amps.pkl: A dictionary containing the amplitudes of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC amplitudes, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>pc_sipscs_freqs.pkl: A dictionary containing the frequencies of spontaneous IPSCs recorded in pyramidal cells of WT and RTT mice.</strong></p> <p>This dictionary is keyed on ['wt', 'mecp2_pos', 'mecp2_neg'] representing whether the pyramidal cell was recorded from a wild-type mouse ('wt') or is an MeCP2 negative or MeCP2 positive RTT cell. This value under each key is an array of IPSC frequencies, one per recorded cell. This data was used to construct Figure 4C in the paper.</p> <p><strong>rois_df.pkl: A multi-index dataframe containing all ROI information for each non-DREADD treated cell in this study (Figures 1-3).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0. The columns are ['centroid', 'cell_boundary', 'annulus_boundary']. The centroid for each cell is a 2-tuple of row, column pixel centroid coordinates. The cell_boundary is a two-column array of row, col boundary points for each ROI. The annulus_boundary is a two-column array of row, column interior points in the annulus. The annulus region excludes points of overlap with nearby cell bodies (See STAR methods of the paper).</p> <p><strong>signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD experiments used in this study (Figs 1-3).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 5771 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This dataframe is used in the construction of Figures 1-3 in the paper.</p> <p><strong>som_behavior_df.pkl: A dataframe of behavior readouts recorded by a camera positioned above the mice in each context chamber.</strong></p> <p>This multi-index dataframe has rows indexed by genotype ('wt' or 'het') and mouse_id. The columns are; sample times (*_time), freezing boolean arrays (*_freeze), x-positions in context chamber (*_x) and y-positions in the context chamber (*_y) for each context in *=('Train', 'Fear', 'Neutral', 'Fear_2', 'Neutral_2'). This dataframe was not used in the paper but may still be useful for further analysis.</p> <p><strong>som_sepsc_amplitudes:</strong> <strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC amplitudes. This data was used in Figure 4E-G.</p> <p><strong>som_sepsc_freqs:</strong> <strong>A dictionary containing the amplitudes of spontaneous EPSCs recorded in SOM cells of WT and RTT mice with and without MeCP2.</strong></p> <p>A dictionary with keys ['som', 'som_rett_pos', 'som_rett_neg'] for WT SOM and RTT-SOM cells with and without MeCP2 respectively. Each value is a list of sEPSC frequencies. This data was used in Figure 4E-G.</p> <p><strong>som_signals_df.pkl: A multi-index dataframe containing calcium signals, inferred spikes and metadata for all Non-DREADD SOM cell recordings used in this study (Figs 5).</strong></p> <p>This dataframe index contains the genotype ('wt', 'het'), the mouse_id, the treatment ('NA'=not applicable since no DREADD used), and the cell index starting from 0 and going up to 710 cells. The columns are ['channels', 'channel', 'num_pages', 'width', 'height', 'bits', 'Train_signals', 'Fear_signals', 'Neutral_signals', 'Cue_signals', 'Fear_2_signals', 'Neutral_2_signals', 'Cue_2_signals', 'Train_spikes', 'Fear_spikes', 'Neutral_spikes', 'Cue_spikes', 'Fear_2_spikes', 'Neutral_2_spikes', 'Cue_2_spikes', 'sample_rate']. The channels are all the recorded channels, the channels is the channel on which ROIs were detected, the width and height are the image dimensions, the bits is the image bit depth of the calcium movie. The *_signals' are the df/f signals for each cell in each context. Each signal is a numpy array with the first 800 samples have been set to NAN due to settling time of the miniscope. The '*_spikes' are the inferred spikes for each cell stored as an image index. This signal and spike indices can be converted to time using the sample column. This data was used to construct Figure 5B-C.</p> <p><strong>ssn33_sstcre_basis.npz: a dict containing three numpy arrays representing the basis images for mouse ssn33 of genotype sst-cre.</strong></p> <p>This dict has three arrays stored under the variable names 'U', 'sigma' and 'img_shape'. U is a matrix of column vector basis images. Each column is the vector representation of a basis image (row pixels x column pixels). There are 100 basis images (columns) in U. The sigma variable is the singular value associated with each basis image vector in U. img_shape can be used to reshape each basis column vector into a 2-D image for viewing. This data is used in Figure 5A of the paper.</p> <p><strong>ssn33_sstcre_sources.npy: A numpy array containing all source images computed from all contexts of the CFC task for mouse N019 of genotype wild-type.</strong></p> <p>This numpy array has shape n x height x width where n=86 source images, height=516 pixels and width=654 pixels. This data was used to construct Figure 5A of the paper.</p>
Water surface occurrence and recurrence from the article "Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction"
<p>This data package contains the 10 m spatial resolution occurrence and recurrence water surface masks from the article "Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction" . These maps have been produced with Sentinel-1 images (10 m) and a Deep Learning method for image segmentation called U-net, methods and data are fully described in the article. Water surface occurrence is computed for the period 2022-2023 and indicates the percentage of time that a pixel is classified as water (100%: always water, 0%: never water, and values between 0 and 100 indicate seasonality). Water surface recurrence is computed for the period 2022-2023 and indicates the number of times that a pixel was classified as a water surface, i.e., 35 indicates that the pixel was classified 35 times as a water surface during the 2022-2023 period. The total size of the dataset is 158 Mo and is distributed in two Geotiffs, one for the water surface occurence and one for the water surface. When using this dataset, please cite the original article <a href="https://doi.org/10.3390/rs16061056">https://doi.org/10.3390/rs16061056</a></p>
Data files for In situ training of feedforward and recurrent convolutional memristor networks
<p>MATLAB data files for the manuscript "<em>In situ training of feedforward and recurrent convolutional memristor networks" </em>published on Nature Machine Intelligence, 2019.</p> <p>The MATLAB data file "exp_mnist.mat" consists of all experimental data on implementing the convolutional neural network with the 1-transistor 1-memristor array that is used for plotting the Figure 1 and 2 of the manuscript.</p> <p>The MATLAB data file "exp_mnistsequence.mat" consists of all experimental data on implementing the convolutional long short-term memory network on the 1-transistor 1-memristor array that is used for plotting the Figure 3 and 4 of the manuscript.</p> <p>The code that generated these data files are provided by the link within the manuscript. Alternatively, the code can be accessed via <a href="https://github.com/zhongruiwang/memristorCNN">https://github.com/zhongruiwang/memristorCNN</a>.</p>
Common biochemical and topological properties of metabolic genes recurrently dysregulated in tumors
<p>Although tumors exhibit numerous metabolic alterations, it’s unclear if common objectives and constraints underlie diverse metabolic changes. Here we interpret cancer gene expression, copy number variation, and survival data using a computational model, MetOncoFit. MetOncoFit evaluates142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topological attributes, and reaction flux. Meta-analysis of tumor databases using MetOncoFit revealed that metabolic enzymes with high catalytic activity were frequently up-regulated in many tumors and associated with poor survival. MetOncoFit also identified metabolites that were hot-spots of dysregulation. MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.</p>
Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution
<p>Data and model files supporting the manuscript: </p> <p>Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution.</p> <p>Repository: https://github.com/alcantarar/Recurrent_GRF_Prediction</p>
Residual dynamics resolves recurrent contributions to neural computation
<p>This repository contains all the neural and simulated datasets used in the above publication. All the data are stored as .mat files.</p> <p>This repository is organized into three main folders. For ease of use, we recommend extracting the contents of each of the three folders into a single folder titled ‘data’.</p> <ol> <li>neuraldata –Raw and preprocessed neural data. Organized into the following folders: <ol> <li>array_TDR_dotsTask_data – contains the non-processed neural data of each session for both monkeys</li> <li>array_dotsTask_beh_xx – contains the behavioral data of each session for both monkeys (xx = monkey)</li> <li>array_reppasdotsTask_datasegmented_binsize=xxms – contains the pre-processed neural data (session wise) of both monkeys for a specified bin size (xx = bin size)<br> </li> </ol> </li> <li>simulations – Data from the simulated models. Organized into the following folders: <ol> <li>toymodels – Simulated data from all models of decisions/movement. Also includes simulations of the augmented line attractor and rotational dynamics models.</li> <li>twoarearnn – Simulated data from the two types of (feedback and nofeedback) two-area RNN models, along with the impulse response simulations.<br> </li> </ol> </li> <li>analyses – contains processed data files from various stages of the analysis pipeline. Only the most important files are listed below: <ol> <li>xx_aligned_reppasdotsTask_binsize=45ms.mat – session-aligned neural data for a specified monkey (xx = monkey)</li> <li>xx_ndim=8_lag=3_alpha=200_50_allconfigsresdynresults.mat – contains the residual dynamics for a specified monkey.</li> <li>xx_aligned_hankelagdimCV_reppasdotsTask_binsize=45ms.mat and xx_aligned_smoothnessCV_reppasdotsTask_binsize=45ms.mat – results of cross-validation of the residual dynamics pipeline for a specified monkey (xx = monkey)</li> </ol> </li> </ol>
Recurrent aphthous stomatitis cycle (RAS).
<p>"Recurrent aphthous stomatitis cycle (RAS Cycle)" . Creator: Esteban Núñez, Chile.</p> <p>Cite: Núñez, Esteban. (2020, April 24). Recurrent aphtous stomatitis cycle. Zenodo. DOI: 10.5281/zenodo.3765796</p>
Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"
<p>Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"</p>
Dataset for Recurrent Rossby wave packets modulate the persistence of dry and wet spells across the globe
<p>This dataset is used in the following study: "Recurrent Rossby wave packets modulate the persistence of dry and wet spells across the globe."</p> <p>Dataset includes:</p> <ul> <li>Dry and wet spells for the Northern and the Southern Hemisphere respectively.</li> <li>The output of the statistical model for each season (MJJASO/NDJFMA) and for each spell type (dry/wet) for both the hemispheres (NH/SH).</li> </ul> <p>File naming used belongs to mainly two categories: one for naming spell file, and second for naming the output file from the statistical model. An example from each category is explained below. The rest of the files follow the same naming style.</p> <ol> <li><em>Spell file</em>;<strong> NH_1.0mm_dry_spells_all_months_gap_1_days_no_spell2_check.nc</strong>: Northern Hemisphere 1.0mm threshold dry spell for all months with a gap of 1 day</li> <li><em>Statisical model output file</em>; <strong>NH_weibull_MJJASO_drythresh_1_min_spell_count_40_time_steps_min_spell_length_5D_1D_N_1980_2016.nc</strong>: Northern Hemisphere Weibull model output for MJJASO using dry threshold of 1.0 mm with a minimum spell count of 40 time-steps and a minimum spell length of 5 days for the period 1980-2016</li> </ol> <p>This dataset alone is sufficient for reproducing the analysis presented in the study. Open source tools like Python, R, etc can be used to read '.nc' file type. Additional code help in the form of Jupyter notebooks reproducing figures made from this dataset can be viewed <a href="https://github.com/avatar101/RRWPS-extremes">here on GitHub.</a></p>
Semi-Recurrent Neural Networks In IllustrisTNG And N-Body Simulations
<p>This is the official data repository for the MNRAS publication <a href="https://arxiv.org/abs/2203.12702">Modelling the galaxy-halo connection using semi-recurrent neural networks</a>, and subsequent works <a href="https://arxiv.org/abs/2409.16548">Optimised neural network predictions of galaxy formation histories using semi-stochastic corrections</a> and <a href="https://arxiv.org/abs/2409.16079">Evaluating the galaxy formation histories predicted by a neural network in pure dark matter simulations</a>. For details on access and utilisation of the data and code, see documentation.pdf in the affiliated <a href="https://github.com/hgc4/TNG-Networks">GitHub repository</a>.</p>
Data for: Brain control of bimanual movement enabled by recurrent neural networks
<p>Brain-computer interfaces have so far focused largely on enabling the control of a single effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion could unlock greater functionality for people with paralysis (e.g., bimanual movement). However, it may prove challenging to decode the simultaneous motion of multiple effectors, as we recently found that a compositional neural code links movements across all limbs and that neural tuning changes nonlinearly during dual-effector motion. In this study, we demonstrate the feasibility of high-quality bimanual control of two cursors via neural network (NN) decoders.</p> <p>This dataset represents all neural activity recorded during these experiments. This includes the neural activity corresponding to unimanual and bimanual hand movements during (1) instructed delay experiments and (2) real-time BCI control of two cursors. </p> <p>Code associated with the data can be found here: <a href="https://github.com/d-r-deo/bimanualBCI" target="_blank" rel="noopener">https://github.com/d-r-deo/bimanualBCI</a></p>
F I G U R E 1 in Echinorhinus brucus (Bonnaterre, 1788) in the Caribbean Sea: A recurrent visitor, or are the artisanal fisheries exploiting deeper waters?
F I G U R E 1 Confirmed georeferenced records (indicated by black dots) and fishing polygon boundaries (represented by dashed black lines) for the bramble shark Echinorhinus brucus (Bonnaterre, 1788) across the western Atlantic (a). A detailed view of the Caribbean Sea region is provided, highlighting the capture locations of bramble sharks, including our specimen (marked by a black star) (b). Key phenotypic diagnostic characteristics are depicted, showcasing the absence of an anal fin, two closely positioned unpinned dorsal fins near the caudal region, and a flat head and snout (c). An image of the specimen's open mouth, revealing its teeth (d). Additionally, a close-up photograph of an extracted tooth (e). Notations for countries: Colombia (Col), the Dominican Republic (D.R.), Mexico (Mex), Nicaragua (Nic), and Venezuela (Ven).
Results Data: Understanding the distributional effects of recurrent floods in the Philippines
<p>This dataset provides all the files required to visualize the main Figures of the paper:</p> <p>Sauer, Inga and Walsh, Brian James and Frieler, Katja and Bresch, David N. and Otto, Christian, Understanding the Distributional Effects of Recurrent Floods in the Philippines (May 27, 2024).</p> <p>The filenames indicate the figures for which the dataset serve as input. The name "haz" indicates a file that provides flooded areas, the final number indicates the admin1 region.</p> <p>keff-> capital stock damage</p> <p>cons_sm-> consumption loss smoothed with savings</p> <p>wb_sm-> well-being loss smoothed with savings</p>
Data of "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step."
<p>Data related to<br> ===========<br> title = "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step.",<br> journal = "Computer Methods in Applied Mechanics and Engineering",<br> volume ="390",<br> year = "2022",<br> doi = "https://doi.org/<a href="http://dx.doi.org/10.1016/j.cma.2021.114476">10.1016/j.cma.2021.114476</a> ",<br> pages = "114476 ",<br> author = "Wu, Ling and Noels, Ludovic"</p> <p>We would be grateful if you could cite the paper in the case in which you are using the data</p> <p> </p> <p>The files replace version 1 whose zip was corrupted.</p> <p> </p>
Classification of blood cells dynamics with convolutional and recurrent neural networks: a sickle cell disease case study
<p>The fraction of red blood cells (RBC) adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease (SCD). Its high-throughput measurement relies on the video analysis of thousands of cell motions for each blood sample to eliminate a large majority of unreliable samples(out of focus or overlapping cells) and discriminate between tank-treading and flipping motion, characterizing highly and poorly deformable cells respectively. These videos are of different durations (from 6 to more than 100 frames).</p> <p>This dataset contains four adult patients with SCD. They were enrolled in the study Drepaforme (approved by the institutional review board CPP Ouest 6 under the reference n°2018A00679-46) and were sampled weekly for several months. The movies were processed using in-house routines in Matlab (Matlab, R2016a) and RBC were detected individually and tracked over time. The database provided in this repository are already pre-processed sequences of tracked and centered RBC over time, each time step image being normalized to 31x31 pixels. Within the 32 experiments, the total number of sequences (or samples) is nearly 150 000. All sequences were semi-automatically labelled into 3 classes, depending on the dynamic of the cell: tank-treading, flipping and unreliable (140 000 are unreliable). The percentage of tank-treading cells with respect to all reliable cells (tank-treading+flipping) in every experiment is the final goal of this study.</p> <p>This dataset is very interesting to the community as it is a large database for cell dynamics classification: the class depends on the movement of the cell.</p> <p>An automatic processing of the database using a 2-stage deep learning model is available here https://github.com/icannos/redbloodcells_disease_classification</p> <p>For opening the data in python:</p> <p> from scipy.io import loadmat<br> x=loadmat('BG20191003shear10s01_Export.mat')</p> <p> * x['Norm_Tab'] is of size nb_samples x max_len_sequences x 31 x 31, where max_len_sequences is the length of the longest sequence of the series, typically ~150 to 180. The other sequences are padded with 31x31 zero matrices at the end in order to fill this maximal length.</p> <p> * x['Labels_Num'] is the corresponding label of each sequence, of size nb_samples. Label can be:<br> - 0 : "tank-treading" (or healthy)<br> - 1 : "flipping" (or tumbling, i.e. related to a SCD)<br> - 2 : "unreliable"</p>
Polymeric Property Prediction Using Recurrent Neural Networks with Optimizations
<p>Database of the Dielectric Polymers used for training and testing of the developed Recurrent Neural Network models (DOI:10.5281/zenodo.4280446)</p>
Cumulative effects doubled the drought impacts for one-fifth of ecosystems exposed to recurrent droughts during Australia's Millennium Drought
<p><strong>Drought-induced vegetation declines have been reported across the globe and may have widespread implications for ecosystem composition, structure, and functions. Thus, it is critical to maximizing our understanding of how vegetation has responded to recent drought extremes. To date, most drought assessments have focused on the impact of a single drought event, emphasizing the importance of drought intensity for vegetation responses. However, drought timing, duration, and repeat exposure may all be important aspects of ecosystem response with the potential for non-linear effects. Cumulative effects are one such phenomenon, representing the additional decline due to repeated or prolonged exposure to drought, and indicating gradual loss of ecosystem resistance. This study quantifies the frequency and magnitude of cumulative effects among Australian ecosystems as they responded to the Millennium Drought. Three distinct biophysical variables derived from satellite remote sensing were analyzed, including fraction of photosynthetically absorbed radiation, photosynthetic vegetation cover, and canopy density derived from passive microwave data. While cumulative effects were detected in only 20% or less of the landscape exposed to repeat or long-duration drought, they approximately doubled drought impacts in the areas where they occurred. Cultivated lands and grasslands were the most susceptible to cumulative effects from recurrent droughts. Despite being relatively infrequent in forests and savannas, cumulative effects from repeated or long-duration drought caused larger additional declines in these ecosystems. Overall, our study demonstrates that the temporal sequence of drought events can strongly influence the magnitude of drought impacts, which provides important insights into modeling vegetation response to recurrent drought events</strong></p>
Point-of-care monitoring of head and neck cancer treatment response and recurrence development using nanopore-based ctDNA consensus sequencing
<p>Circulating tumor DNA (ctDNA) in blood may become a generic biomarker for non-invasive cancer diagnosis and monitoring. However, detection of ctDNA is challenged by the presence of many circulating DNA molecules from healthy cells. We found that single ctDNA molecules can be sequenced with high accuracy by a three-step process consisting of capturing, copying and concatenation of the original double-stranded ctDNA molecules. This innovative approach - called CyclomicsSeq - is unparalleled by any other method in terms of cost-efficiency and speed, allowing point-of-care cancer diagnostics.</p> <p>Within this CPOC, subsidized by the Oncode institute, we have applied our CyclomicsSeq ctDNA test in patients with advanced head and neck cancer squamous cell carcinoma (HNSCC). Head and neck cancer (HNSCC) accounts for 380,000 cancer-related deaths worldwide. For these patients, determining whether a patient responds to the primary chemoradiation treatment is challenging, and non-responders are sometimes identified when other treatment options are no longer possible. By measuring the ctDNA levels in the blood of these patients prior to and during treatment, we aim to identify non-responders at an earlier stage.</p> <p>This dataset contains base calls of TP53 of 47 nanopore sequencing runs. We included 10 patients and 7 controls. For the patients, we have samples of multiple time points (0 = prior to treatment, 1 = 1 week after treatment initiation, etc).</p>
Data from journal article "Amplifying effects of recurrent drought on the dynamics of tree growth and water use in a subalpine forest"
<p>This dataset contains all data used in the above mentioned publication (DOI: tba). The data was obtained in a field drought experiment in a subalpine forest in the Austrian Central Alps, where adult trees were exposed to either ambient conditions or recurrent summer drought. The experiment was conducted from 2016-2018. All data were measured on individual tree level. All dataset contains the following, pre-processed data (processed as described in the corresponding journal article):<br> - Micrometeorological data<br> - Dendrometer data<br> - Tree ring data<br> - Sapflow data<br> - Stem electrical resistivity data<br> - Non-structural carbohydrate (NSC) and soluble sugar carbon isotopic composition (d13C) data</p> <p>The metadata.xlsx file contains all metadata, including units and descriptions of the variables.</p>
Pixelated Reconstruction of Gravitational Lenses using Recurrent Inference Machine
<p>Modeling strong gravitational lenses to quantify the distortions of the background sources and reconstruct the mass density in the foreground lens has traditionally been a major computational challenge. As the quality of gravitational lens images increases, the task of fully exploiting the information they contain becomes computationally and algorithmically more difficult. </p> <p>In order to tackle this challenge, we have trained a Recurrent Inference Machine (RIM) on a large dataset of realistic gravitational lenses simulated from galaxy images from the COSMOS survey and convergence maps from the IllustrisTNG simulation. This dataset contains the dataset used to train the RIM and the Variational AutoEncoder (VAE) used in our work, <a href="https://arxiv.org/abs/2207.01073">Pixelated Reconstruction of Gravitational Lenses using Recurrent Inference Machine</a>, presented at the Machine Learning for Astrophysics Workshop at the Thirty-ninth International Conference on Machine Learning (ICML 2022), as well as the checkpoint files for the neural networks. </p> <p>The source code for our work is published under the package name <a href="https://github.com/AlexandreAdam/Censai">Censai</a>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.